Exploring the Adversarial Frontier: Quantifying Robustness via Adversarial Hypervolume

arXiv:2403.05100v1 Announce Type: new
Abstract: The escalating threat of adversarial attacks on deep learning models, particularly in security-critical fields, has underscored the need for robust deep learning systems. Conventional robustness evaluations have relied on adversarial accuracy, which measures a model's performance under a specific perturbation intensity. However, this singular metric does not fully encapsulate the overall resilience of a model against varying degrees of perturbation. To address this gap, we propose a new metric termed adversarial hypervolume, assessing the robustness …

accuracy adversarial adversarial attacks arxiv attacks critical cs.ai cs.cr cs.cv cs.lg deep learning frontier metric performance robustness security systems threat under